1. Field of the Invention
The present invention relates to a technical field of intelligent decision. More particularly, the present invention relates to an intelligent decision supporting system and a method for making intelligent decision.
2. Description of the Related Art
With the development of technologies that provide information, such as the internet, the processing speed and volume of various types of information that are received and processed by people are rapidly increasing. When receiving information from different channels, for example, digital document information such as a webpage on the internet, an email, a digital library etc., people need to make judgments and decisions according to the information provided by these digital documents. It is an important subject in the field of digital document processing to classify text so as to efficiently and quickly process the digital document. Text classification refers to the construction of a model for classification based on the available data, i.e. a classifier. A classifier determines a category for each document in a set of test documents according to a predefined classification system, such that a user is able to conveniently browse a document, or to facilitate searching for documents by limiting the scope of searching Automatic text classification refers to training of a classification rule or modeling parameters by using a large amount of text with class tags, and recognize text of an unknown category by using the result of the training. Support Vector Machine (SVM) is a well-known method for text classification, and is widely used. SVM is a pattern recognition method based on statistics and learning theory, which shows special advantages in resolving problems of pattern recognition of small sample, non-linear and high dimensions, and can be applied to other machine learning problems such as function fitting. SVM is now successfully applied to many fields such as Bioinformatics, text and handwriting recognition etc.
A current text classifier is only used to classify text or insert a label to text for classification. More particularly, the text classifier first collects data according to predefined classification levels to form a large quantity of training samples. Then, the text classifier performs feature extraction and model training on the training samples to generate a model of text category. Next, the text classifier may classify text to be predicted by using the model obtained by training. In particular, the text classifier pre-processes the text to be predicted, extracts features of the text, and classifies the text by using the generated model. The text classifier outputs a confidence rate for each category, and classifies the text to be predicted into a plurality of categories according to the confidence rate, or adds a label to the text to be predicted and classifies it.
However, a problem exists in the related art in that the categories into which the text is classified by the text classifier are predefined tags, which cannot be used to make an intelligent decision. That is, it is unable to obtain a decision related to the text through text classification. Thus, an intelligent decision supporting system, which may predict an intention or interest of a client by text classification and other techniques of related art, and provide a feedback opinion or hint to help the user/client to make a decision is needed.